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    Home / Central Data Catalog / DDI-IND-NSSO-66-SCHEDULE-1.0T1 / variable [V313]
central

Household Consumer Expenditure Type-1, July 2009 - June 2010
NSS 66th Round

India, 2009 - 2010
Get Microdata
Reference ID
DDI-IND-NSSO-66-SCHEDULE-1.0T1
Producer(s)
National Sample Survey Office,NSSO
Collections
Household Consumption Expenditure
Metadata
DDI/XML JSON
Created on
Jan 18, 2018
Last modified
Mar 27, 2019
Page views
219316
Downloads
21322
  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • Data files
  • Identification
    of Sample
    Household
  • Household
    Characteristics
  • Demographic and
    other
    particulars of
    household
    members
  • Consumption of
    cereals,
    pulses, milk
    and milk
    products, sugar
    and salt during
    the last 30
    days
  • Summary of
    comsumer
    expenditure
  • Consumption of
    clothing,
    bedding and
    footwear during
    last 30 and
    365 days
  • Expenditure on
    education and
    medical
    (institutional)
    goods and
    services
  • Expenditure for
    purchase and
    construction
    (including
    repair and
    maintenance) of
    durable goods
    for domestic
    use
  • Expenditure on
    miscellaneous
    goods and
    services
    including
    medical
    (non-institutional),
    rents and taxes
    during the last
    30 days

FOD Sub-Region (FOD_Sub_Region)

Data file: Identification of Sample Household

Overview

Valid: 100847
Invalid: 0
Type: Discrete
Start: 40
End: 43
Width: 4
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
0000 7407
7.3%
0014 8
0%
0024 8
0%
0030 8
0%
0046 8
0%
0093 16
0%
0100 8
0%
0110 Jammu 815
0.8%
0111 Udhampur 312
0.3%
0120 Srinagar 584
0.6%
0121 Anantnag 556
0.6%
0122 Baramula 430
0.4%
0130 8
0%
0170 8
0%
0182 8
0%
0210 Shimla 643
0.6%
0211 Bilaspur 456
0.5%
0212 Dharamshala 510
0.5%
0213 Mandi 440
0.4%
0231 24
0%
0282 8
0%
0310 Jalandhar 486
0.5%
0311 Amritsar 320
0.3%
0312 Firozpur 608
0.6%
0313 Hoshiarpur 376
0.4%
0320 Ludhiana 566
0.6%
0321 Bathinda 248
0.2%
0322 Patiala 511
0.5%
0330 8
0%
0331 8
0%
0380 8
0%
0500 8
0%
0510 Dehradun 1012
1%
0511 Almora 767
0.8%
0610 Chandigarh 401
0.4%
0611 Ambala 484
0.5%
0612 Bhiwani 344
0.3%
0613 Hisar 416
0.4%
0614 Karnal 600
0.6%
0615 Rohtak 680
0.7%
0706 8
0%
0710 Delhi 901
0.9%
0810 Ajmer 445
0.4%
0811 Jodhpur 640
0.6%
0812 Udaipur 704
0.7%
0820 Jaipur 1007
1%
0821 Alwar 367
0.4%
0822 Ganganagar 365
0.4%
0823 Kota 608
0.6%
0910 Agra 880
0.9%
0911 Aligarh 510
0.5%
0912 Meerat 510
0.5%
0920 Allahabad 446
0.4%
0921 Azamgrah 584
0.6%
0922 Faizabad 447
0.4%
0923 Gorakgpur 776
0.8%
0924 Varanasi 681
0.7%
0930 Bareilly 552
0.5%
0931 Moradabad 544
0.5%
0932 Sitapur 368
0.4%
0933 Sharanpur 472
0.5%
0940 Lucknow 480
0.5%
0941 Fatehpur 448
0.4%
0942 Gonda 448
0.4%
0943 Jhansi 512
0.5%
0944 Kanpur 288
0.3%
1010 Muzaffarpur 672
0.7%
1011 Darbhanga 608
0.6%
1012 Motihari 528
0.5%
1013 Purnia 872
0.9%
1020 Gaya 920
0.9%
1021 Bhagalpur 459
0.5%
1022 512
0.5%
1050 8
0%
1110 Gangtok 768
0.8%
1210 32
0%
1211 16
0%
1281 8
0%
1310 Kohima 1024
1%
1311 Imphal 8
0%
1400 8
0%
1410 48
0%
1420 8
0%
1513 8
0%
1710 Shillong 792
0.8%
1711 Tura 480
0.5%
1712 Agartala 0
0%
1810 Guwahati 1192
1.2%
1811 Silchar 600
0.6%
1820 Dibrugarh 256
0.3%
1821 Jorhat 696
0.7%
1822 Tezpur 664
0.7%
1900 8
0%
1910 Barddhaman 536
0.5%
1911 Bankura 488
0.5%
1912 Chinsura 760
0.8%
1913 Medinipur 656
0.7%
1919 8
0%
1920 Kolkata 1670
1.7%
1921 Howrah 384
0.4%
1930 Maldah 600
0.6%
1931 Behrampur 608
0.6%
1932 Siliguri 608
0.6%
2010 Ranchi 824
0.8%
2011 Dumka 680
0.7%
2012 Hazaribag 848
0.8%
2013 Jamshedpur 387
0.4%
2019 8
0%
2021 8
0%
2110 Bhubaneswar 448
0.4%
2111 Baripada 480
0.5%
2112 Behrampur 480
0.5%
2113 Cuttak 1055
1%
2120 Sambalpur 855
0.8%
2121 Bhawanipatn 704
0.7%
2210 Raipur 760
0.8%
2211 Ambikapur 512
0.5%
2212 Bilaspur 472
0.5%
2213 Durg 480
0.5%
2222 8
0%
2224 8
0%
2310 Bhopal 508
0.5%
2311 Chhindwara 447
0.4%
2312 Khandwa 268
0.3%
2313 Indore 356
0.4%
2320 Gwalior 400
0.4%
2321 Ratlam 368
0.4%
2322 Shivpuri 454
0.5%
2323 Ujjain 455
0.5%
2330 Jabalpur 377
0.4%
2331 Rewa 608
0.6%
2332 Sagar 416
0.4%
2410 Ahmedabad 478
0.5%
2411 Bhavnagar 288
0.3%
2412 Jamnagar 224
0.2%
2413 Rajkot 384
0.4%
2414 Surendranaga 183
0.2%
2420 Baroda 670
0.7%
2421 Mahesana 318
0.3%
2422 Nadiad 339
0.3%
2423 Surat 372
0.4%
2424 Valsad 480
0.5%
2620 8
0%
2710 Aurangabad 576
0.6%
2711 Jalgaon 575
0.6%
2712 Nandel 640
0.6%
2713 Nashik 384
0.4%
2720 Mumbai 925
0.9%
2721 Thane 737
0.7%
2722 8
0%
2730 Nagpur 957
0.9%
2731 Akola 416
0.4%
2732 Amravati 448
0.4%
2740 Pune 1274
1.3%
2741 Kolhapur 607
0.6%
2742 Solapur 440
0.4%
2790 8
0%
2810 Cuddapah 616
0.6%
2811 Anantapur 320
0.3%
2812 Guntur 368
0.4%
2813 Kurnool 312
0.3%
2814 Nellore 512
0.5%
2820 Hyderabad 1452
1.4%
2821 Karimnagar 512
0.5%
2822 Nizamabad 400
0.4%
2823 Warangal 520
0.5%
2830 Vijayawada 672
0.7%
2831 Kakinada 416
0.4%
2832 Visakhapatnam 800
0.8%
2833 Guwahati 0
0%
2874 8
0%
2910 Bangalore 988
1%
2911 Mangalore 351
0.3%
2912 Mysore 415
0.4%
2913 Shimoga 368
0.4%
2920 Hubli 639
0.6%
2921 Belgaum 470
0.5%
2922 Bellary 512
0.5%
2923 Gulbarga 319
0.3%
2924 Panaji 8
0%
3010 444
0.4%
3210 Kozhikode 976
1%
3211 Kannur 584
0.6%
3212 Palakkad 343
0.3%
3213 Thrissur 455
0.5%
3220 Thiruvanathapuram 479
0.5%
3221 Kochi 614
0.6%
3222 Kollam 800
0.8%
3223 Kottayam 416
0.4%
3310 Coimbatore 848
0.8%
3311 Dharmapuri 288
0.3%
3312 Salem 480
0.5%
3313 Tiruchirappali 736
0.7%
3320 Chennai 959
1%
3321 Cuddalore 480
0.5%
3322 Vellore 551
0.5%
3323 Pondicherry 512
0.5%
3330 Madurai 608
0.6%
3331 Thanjavur 512
0.5%
3332 Tirunelveli 664
0.7%
3333 Virudhunagar 504
0.5%
3510 Port Blair 560
0.6%
7710 8
0%
8713 8
0%
9101 7
0%
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
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